Implementation of a dynamic adjustment mechanism with efficient replica selection in data grid environments

Yang, Chao‐Tung; Yang, I-Hsien; Chen, Chun-Hsiang; Wang, Shih-Yu

doi:10.1145/1141277.1141458

Cited by 21 publications

(21 citation statements)

References 12 publications

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“…The objective of the first aspect is to enhance selecting process by new methods which select set of replica servers instead of one replica provider site [1], whereas the second aspect aims to improve the efficiency of transferring the requested files between the selected computing sites and replica provider sites [12].…”

Section: Long-term Optimizationmentioning

confidence: 99%

“…The first direction that has been followed by researcher is focused on minimizing the time that consumes in the selecting set of best replica sites, so, they proposed different selection strategies used by Replica Optimization Service to enhance selection process [2]. The objective of the other direction is to exploit rate differences among links of various computing data grid site and replica data grid providers and also to divide the requested files into multiple blocks sizes for improving the efficiency of transferring files in grids [12]. When the files dividing into multiple parts, then co-allocation architecture is used to transfer the files in parallel from multiple replica provider sites.…”

Section: Introductionmentioning

confidence: 99%

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Taxonomy of Optimization Approaches of Resource Brokers in Data Grids

M.Almuttairi¹

2015

IJCSIT

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Section: Long-term Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Taxonomy of Optimization Approaches of Resource Brokers in Data Grids

M.Almuttairi¹

2015

IJCSIT

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“…The Globus Toolkit provides solutions for such considerations as security, resource management, data management, and information services. One of its primary components, MDS [6,11,16,26], is designed to provide a standard mechanism for discovering and publishing resource status and configuration information. It provides a uniform and flexible interface for data collected by lower-level information providers in two modes: static (e.g., OS,…”

Section: The Co-allocation Architecturementioning

confidence: 99%

“…initialPT ≤ ClientMaxBandwidth /Number of Replica Source (1) In our previous work [23,25,26,27,28], we proposed a replica selection cost model in which we defined a formula for calculating the weighting. First, we get a score based on the states of the various server devices:…”

Section: The Dynamic Adjustment Strategymentioning

confidence: 99%

“…This method selects the servers most likely to provide optimum transfer rates because bandwidth quality can vary unpredictably due to the sharing nature of the Internet. Another way is to use co-allocation technology [17,21,23,24,25,26,27,28,30] to download data. Co-allocation of data transfers enables the clients to download data from multiple locations by establishing multiple connections in parallel.…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Adjustment Strategy for File Transformation in Data Grids

Yang

Wang

2007

Lecture Notes in Computer Science

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Abstract. In this paper, we propose a dynamic file transfer scheme with coallocation architecture, called Dynamic Adjustment Strategy, a dynamic file transfer scheme with co-allocation architecture that reduce the file transfer times and improves the performance in Data Grid environments. Our approach reduces the idle time faster servers spend waiting for the slowest server, and decreases file transfer completion time. We also present a new toolkit, called Cyber-Transformer, with a friendly graphical user interface interface running on the client side that makes it easy for inexperienced users to manage replicas and download the files in Data Grid environments. We also provide an effective scheme for reducing the cost of reassembling data blocks.

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An Anticipative Recursively Adjusting Mechanism for parallel file transfer in data grids

Yang

Chi

et al. 2010

Concurrency and Computation

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SUMMARYData Grids enable the sharing, selection, and connection of a wide variety of geographically distributed computational and storage resources for content needed by large-scale data-intensive applications such as high-energy physics, bioinformatics, and virtual astrophysical observatories. In Data Grids, co-allocation architectures were developed to enable parallel downloads of data sets from selected replica servers. As Internet is usually the underlying network of a grid, network bandwidth plays as the main factor affecting file transfers between clients and servers. In this paradigm, there are still some challenges that need to be solved, such as to reduce differences in finish times between selected replica servers, to avoid traffic congestion resulting from transferring the same blocks in different links among servers and clients, and to manage network performance variations among parallel transfers. In this paper, we propose the Anticipative Recursively Adjusting Mechanism (ARAM) scheme to adjust the workloads on selected replica servers and handle unpredictable variations in network performance by those servers. Our algorithm is based on using the finish rates for previously assigned transfers to anticipate the bandwidth status for the next section to adjust workloads, and to reduce file transfer times in grid environments. Our approach is useful in grid environments with unstable network link. It not only reduces idle time wasted waiting for the slowest server, but also decreases file transfer completion times.

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Implementation of a dynamic adjustment mechanism with efficient replica selection in data grid environments

Cited by 21 publications

References 12 publications

Taxonomy of Optimization Approaches of Resource Brokers in Data Grids

Taxonomy of Optimization Approaches of Resource Brokers in Data Grids

A Dynamic Adjustment Strategy for File Transformation in Data Grids

An Anticipative Recursively Adjusting Mechanism for parallel file transfer in data grids

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